Curating proxy server pools

ABSTRACT

A system and method of forming proxy server pools is provided. The method comprises several steps, such as requesting a pool to execute the user&#39;s request and retrieving an initial group. The system checks the service history of an initial group, including whether any of the proxy servers in an initial group are exclusive to existing pools. The exclusive proxy servers in an initial group with eligible proxy servers are replaced when needed and new proxy server pools are formed. The system also records the service history of proxy servers and pools before and after the pools are created. The method can also involve predicting the pool health in relation with the thresholds foreseen and replacing the proxy servers below the threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/006,499, filed Aug. 28, 2020, which is incorporated herein byreference in its entirety.

FIELD OF THE INVENTION

The disclosure belongs to the area of data scraping. More specifically,the disclosures present an efficient way to analyze the history of theexit nodes, organized in groups, or pools, and predict their performanceand behavior as a group to receive advanced web scraping results.

BACKGROUND OF THE INVENTION

A proxy server acts as an intermediary for requests from the userseeking resources from target servers. A user connects to the proxyserver, requesting data. The proxy server evaluates the request andforwards the request to the actual target system or systems containingthe corresponding data. After obtaining the data, the proxy servernormally forwards the data to the original requestor, but other actionscan also be performed by the proxy (e.g., return error message, etc.).Depending on the type of request, a proxy server may or may not havefull visibility into the actual content fetched to the originalrequestor, e.g., in case of an encrypted Hypertext Transfer ProtocolSecure (HTTPS) session, the proxy may serve as an intermediary, blindlyforwarding the data without being aware of what is being forwarded.

The proxies can be divided into different types depending on whatfunctions are provided or what servers are used. The proxies can also bedivided into Residential Internet Protocol (IP) proxies, Datacenter IPproxies, and Mobile IP proxies. A Residential IP address is an addressfrom the range specifically designated by the owning party as assignedto private customers. Usually a Residential proxy is an IP addresslinked to a physical device, for example, mobile phone or desktopcomputer, however businesswise the blocks of Residential IP addressesmay be bought from the owning Proxy Service Provider by another companydirectly, in bulk. The real owners of the Residential IP address ranges,namely Internet service providers (ISPs), register residential IPaddresses in public databases, which allows websites to determine adevice's internet provider, network, and location. Datacenter IP proxyis the proxy server assigned with a datacenter IP. Datacenter IPs areIPs owned by companies, not by individuals. The datacenter proxies areactually IP addresses that are not located in a natural person's home.Instead, the datacenter proxies are associated with a secondarycorporation. Mobile IP proxies may be considered a subset of theResidential proxy category. A mobile IP proxy is essentially one IPaddress that is obtained from mobile operators. Mobile IP proxies usemobile data, as opposed to a residential proxy that uses broadband ISPsor home Wi-Fi. When a user sends a request, the request goes to theproxy server first. The proxy server then makes the request on theuser's behalf, collects the response from the web server, and forwardsthe web page data so that the user can access the page. When the proxyserver forwards the requests, it can make changes to the data but yetstill provide the data requested. A proxy server changes the user's IPaddress, so the web server is not provided with the geographicallocation of the user. A proxy can encrypt the user's data so that theuser's data becomes unreadable in transit. Also, a proxy server canblock access to certain web pages, based on IP address, domain name, orthe communication protocol parameters, such as the port requested.

Exit node proxies, or simply exit nodes, are gateways where the traffichits the Internet. There can be several proxies used to perform a user'srequest, but the exit node proxy is the final proxy that contacts thetarget and forwards the information from the target to the user. Therecan be several proxies serving the user's request, forming a proxychain, passing the request through each proxy, with the exit node beingthe last link in the chain that ultimately passes the request to thetarget.

In the current embodiments, proxies and exit nodes can be used assynonyms. The current embodiments are not limited only to the exit nodesand same technologies can be used for proxies of different types.However, the term exit node is employed in the current description toclarify the functional differences between exit nodes and proxies.

A proxy provider can control the quality of proxies and decide which IPaddresses are going to be used for users in a set of proxies. If thesame proxy is used for too many requests, it will ultimately be bannedby the ISP or the web page and it will not be possible to use such aproxy to make subsequent requests. If too many requests come in from oneIP address in a short period of time, then the site will block therequests from that proxy for a pre-set period of time.

This problem is most often encountered in web scraping. Web scraping(also known as screen scraping, data mining, web harvesting) in its mostgeneral sense is the automated gathering of data from the Internet. Moretechnically, it is the practice of gathering data from the internetthrough any means other than a human using a web browser or a programinteracting with an application programming interface (API). Webscraping is usually accomplished by writing a program that queries a webserver and requests data automatically, then parses the data to extractthe requested information.

Web scrapers—programs written for web scraping—can have a significantadvantage over other means of accessing information, like web browsers.The latter are designed to present the information in a readable way forhumans, whereas web scrapers are excellent at collecting and processinglarge amounts of data quickly. Rather than opening one page at a timethrough a monitor (as web browsers do), web scrapers are able to viewlarge databases consisting of thousands or even millions of pages atonce.

Sometimes the website allows another automated way to transfer itsstructured data from one program to another via an API. Typically, aprogram will make a request to an API via Hypertext Transfer Protocol(HTTP) for some type of data, and the API will return this data from thewebsite in the structured form. It serves as a medium to transfer thedata. However, using APIs is not considered web scraping since the APIis offered by the website (or a third party) and it removes the need forweb scrapers.

An API can transfer well-formatted data from one program to another andthe process of using it is easier than building a web scraper (a bot) toget the same data. However, APIs are not always available for the neededdata. Also, APIs often use volume and rate restrictions and limit thetypes and the format of the data. Thus, a user would use web scrapingfor the data for which an API does not exist or which is restricted inany way by the API.

Usually, web scraping includes the following steps: retrieving HypertextMarkup Language (HTML) data from a website; parsing the data for targetinformation; saving target information; repeating the process if neededon another page. A program that is designed to do all of these steps iscalled a web scraper. A related program—a web crawler (also known as aweb spider)—is a program or an automated script which performs the firsttask, i.e. it navigates the web in an automated manner to retrieve rawHTML data of the accessed web sites (the process also known asindexing).

There are techniques that websites use to stop or slow down a hot sincescraping may overload the website. For example, they may try to identifythe IP address of the bot and block it to prevent further access by thebot. To do that, the website needs to identify the hot-like behavior ofthe web scraper and to identify its IP address.

Recognizing the hot-like behavior can be done in multiple ways. Oneinvolves a limit on the rate of actions (or actions over time) sincehumans normally perform less actions than a bot would. To circumventthis, web scrapers often choose to employ proxies which mask the real IPaddress of the web scraper and perform web scraping through multipleproxy IP addresses at the same time to both keep up the gathering speedand avoid being blocked.

Another instance, in which the quality of the proxy is important ismedia streaming. Media can be distributed on the internet in one ofthree ways: full download, progressive download, and streaming. Fulldownload has the disadvantage of having to wait until the download iscomplete to view the media. It is only usable for non-real-time media.Progressive download is applied to the parts of the media progressively,so the user can begin viewing the media before it is fully downloaded.Progressive download is another method for non-real-time mediadistribution because a significant pre-load is usually required to beginviewing the media. Streaming, conversely, is real-time mediadistribution. It works by filling out a playout buffer at the client(usually a few seconds) and playing from the buffer as soon as it fillsup. The advantage of streaming is almost instantaneous distribution ofthe media content.

However, streaming is extremely susceptible to network impairments, likepacket loss and jitter. More specifically, if the rate of transmissionof the media is lower than the playback rate, the playback buffer runsout and the media freezes until the buffer is filled out again.

Media streaming companies strive to offer an uninterrupted experience toits users. They want playback to start instantly and to not stopunexpectedly in any network environment. One way to do that is to reducethe buffer size by compressing the media in more efficient ways. Theother way is to ensure that the connection is stable and reliable andthe servers can handle the load.

A key issue in media streaming is connection reliability. Reliability,formally speaking, is the ability of the connection to function understated conditions for a period of time. Put simply, reliability means aconnection should work and continue working in a way that supportscontinuous streaming of the media.

The same criteria apply to proxy connections. When a proxy user requestsmedia streaming, the proxy can become the bottleneck for the user'sconnection. This means that the proxy user's media streaming connectionwill be as fast and as reliable, as the exit node's connection is. Thus,a proxy provider has an interest in finding the fastest and mostreliable exit nodes for the user that requests media streaming. If theexit node's speed is slow or unreliable, the user will experiencejitters, stoppage, or a drop in media quality.

Proxy providers face a set of problems related to both scraping andstreaming activities. For scraping, it is important to provide the userwith the exit nodes that have a lower probability of being blocked. Forstreaming, it is important that the exit nodes provided to the user arereliable and fast. Otherwise, the media streaming experience will behindered because of the quality of the proxies used.

Moreover, if the exit node disconnects during a session, thusinterrupting the established path toward the content provider, the userhas to reconnect to the streaming service. If upon reconnection the useris being assigned a different exit node, the website may demand torepeat authentication or to confirm human interaction. The repeatedauthentication also interrupts the quality of service.

To solve at least these problems, in one aspect, the present embodimentsdetailed herein store and employ an exit node (a proxy) performance andattribute history at a service provider's side. By analyzing an exitnode's history, a service provider is able to both heuristically predictthe performance and reliability of the exit nodes that are beingassigned as well as ensure that the same exit nodes are reserved for auser over time, thus maximizing the efficiency of the exit node pool.

SUMMARY OF THE INVENTION

The present embodiments analyze the history of the exit nodes, organizedin groups, or pools, and predicting their performance and behavior as agroup.

In one instance, the history allows proxy service providers to assign anexclusive proxy pool to a user, where the pool is not shared with otherusers. In another instance, it allows to heuristically predict theperformance and behavior of the exit nodes, or groups of exit nodes, sothat new users are assigned the potentially best fitting exit nodes. Theheuristic prediction also allows to predict a risk factor associatedwith connection reliability and to address these risks before they crossa certain threshold assigned to each user.

The success of web scraper operations highly depends on the scrapingfunctionality presenting their activities as organic traffic to thewebsite. The proxy service provider can help the scraping request appearorganic and human-like by relaying said requests through exit nodes thatexhibit attributes typical of organic users. This is enabled bycollecting historical data of the exit node, such as its geographicallocation, IP type, and usage history with a target website. Usingcollected and aggregated historical data, a proxy provider is able topredict which exit nodes will be more successful at scraping aparticular website at a given time or in the future.

Similarly, in at least one embodiment, historical data is used topredict which exit nodes will have the best speed and reliabilityrequired by streaming activities through a proxy server. Using thecollected historical data about an exit node's average speed, averageuptime, connection time and duration, average amount of traffic per day,variations in which median and percentile groups are used instead ofaverage values, and other similar attributes the service provider isable to heuristically predict which exit nodes will provide the fastestand the most reliable connection for a streaming session.

Once the best fitting exit nodes are identified, the service providerensures that these exit nodes are exclusive to the user that made theinitial request. Otherwise, the best rated proxies would be assigned adisproportionately heavy load and their performance would decrease. Oncean exit node disconnects from the current pool, another one is takenfrom the exclusive group. If no proxies are available in the exclusivegroup, a new one is found in the non-exclusive groups and assigned toexclusive. This ensures that a user is always working with the bestquality exclusive exit nodes and the efficient utilization of the exitnodes is maximized.

In at least one embodiment, exit nodes report their utilization data tothe proxy server management platform which aggregates the data and usesit to predict future transformations of the current pool. It predictswhether the pool will increase or decrease in quality over a period oftime. This enables a proxy server provider to refill the exclusive groupcandidate queue before the current exit nodes disconnect, thusaddressing connection reliability risks before they cause issues for theuser.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of an exemplary architectural depiction ofcomponents.

FIG. 2 shows a functional diagram of the pool health rating mechanism.

FIG. 3 shows a functional diagram of the exit node quality ratingmechanism.

FIG. 4A shows an exemplary flow diagram of exit node rotation withoutheuristic prediction.

FIG. 4B shows the continuation of the exemplary flow diagram of exitnode rotation without heuristic prediction.

FIG. 5 shows an exemplary flow diagram of exit node rating withheuristic prediction.

FIG. 6A shows an exemplary flow diagram of exit node rotation withheuristic prediction.

FIG. 6B shows the continuation of the exemplary flow diagram of exitnode rotation with heuristic prediction.

FIG. 7 shows an exemplary flow diagram of gateway response to an exitnode's registration barriers.

FIG. 8 shows a block diagram of an exemplary computing system.

DETAILED DESCRIPTION OF THE INVENTION

Some general terminology descriptions may be helpful and are includedherein for convenience and are intended to be interpreted in thebroadest possible interpretation.

Exit Node Management Gateway (also Gateway) 106—is a processing unitwithin the service provider infrastructure that communicates with boththe User's Device that sends requests to it and with Exit Nodes thatultimately service these requests. Furthermore, it stores and consultsresults in the User Database about exit node pool formation. It can alsotrigger actions in the Exit Node Rating Logic And Processing Unit(shorter—Logic Unit).

User Database 108—is a memory storage that stores information about theUser Device's service level and requests. Service level can beformulated through a Service Level Agreement (SLA) between the user andthe service provider. Examples of SLA parameters include agreed servicetargets, criteria for target fulfilment evaluation, roles andresponsibilities of Service Provider, duration, scope and renewal of theSLA contract, supporting processes, limitations, exclusions anddeviations. In addition to the information regarding service level, UserDatabase stores technical parameters about exit nodes or exit node poolsthat fulfill the SLA, for example service speed, reliability, responsetime, traffic load, schedule, compatibility with third party services(like a certain target website), and others. The translation of SLAparameters to technical parameters can be done in a variety of ways,including ad hoc human decision, machine interpretation, direct userinteraction, and others. This does not change the overall functioning ofthe embodiments. In what follows, the concept of ‘parameters’ refersgenerally to the technical parameters of the exit nodes required tomaintain the SLA.

Exit Node Database 110—is a memory storage that stores information aboutthe Exit Nodes. Some exemplary parameters stored in this databaseinclude but are not limited to an exit node's geographical location,connection type, consent to participate in a distributed ContentDelivery Network (CDN) access model, current speed, traffic per day,session runtime, battery life, and others. CDN is a geographicallydistributed group of servers which work together to deliver Internetcontent.

Exit Node Rating Logic And Processing Unit (Logic Unit) 112—is aprocessing unit within the service provider infrastructure thatcommunicates with Exit Node Management Gateway that sends requests to itand with Exit Node Database, History Database, User Database, andTesting Module which contain information to be processed. Exit NodeRating Logic And Processing Unit is primarily responsible for analyzing(processing) information about exit nodes, pools, and user's requestparameters and finding the best fit for them. Examples of processingoperations include but are not limited to grouping data in categories,forming series of data (ordered, partially ordered or unordered),aggregating data, extracting aggregated results, performing statisticalanalysis, running machine learning and deep learning algorithms, formingpredictive models, and other processing functions. In some embodiments,the Logic Unit contains two related mechanisms—Pool Health RatingMechanism and Exit Node Quality Rating Mechanism. These two mechanismscan be physically contained in the same machine as the Logic Unit butthey can also be placed in other devices in which the Logic Unit caninteract with them.

History Database 114—is a memory storage that stores informationextracted from the Exit Node Database and User Database by the Exit NodeRating Logic And Processing Unit about the Exit Nodes' and User'sDevice. It stores two types of information. The first type ofinformation is service history that relates a particular user's devicewith particular exit nodes or pools of exit nodes and once the serviceends for the user device, service history can be deleted, archived oraccumulated but not used. The second type of information is aggregatedhistory that includes but is not limited to aggregated data about ExitNodes' overall performance over their entire history or a period oftime. Some exemplary parameters stored in aggregated history are averagespeed, average response time (globally or to a particular service),average uptime, average schedule (connect and disconnect timesignatures), changes in geographical location, changes in connection andIP type, changes in consent to participate in a distributed CDN accessmodel, variations in which median and percentile groups are used insteadof average values, and others.

Testing Module 118—is a processing unit that performs tests against exitnodes according to a number of parameters, including but not limited togeographical location, connection type, consent to participate in adistributed CDN access model, current speed, traffic per day, sessionruntime, and others.

Pool Health Rating Mechanism (PHRM)—is a mechanism contained in and usedby the Logic Unit that calculates and predicts the pool health of aparticular exit node pool. It also sets a minimal acceptable pool healththreshold for the pool's health and if pool health falls below or ispredicted to fall below the minimal acceptable pool health threshold,the Logic Unit informs the Gateway and the Gateway requests that newexit nodes be added to the pool.

Exit Node Quality Rating Mechanism (ENQRM)—is a mechanism contained inand used by the Exit Node Rating Logic And Processing Unit thatcalculates the fitness of a particular exit node for a particular poolbased on the Improvement Score (IS) that results from the predicted poolhealth with and without an exit node.

Quality—is an aggregate criterion in the PHRM that encapsulates all theparameters defined by the user request parameters. It includes but isnot limited to parameters such as minimal response time in general orfor a specific target, speed (throughput), maximum latency (the delaybefore a transfer of data begins), schedule, reliability, pool impact,and other such parameters. They are all given a specific weight in thecalculation of quality which is a criterion aggregated from the pre-setparameters.

Pool Health—is an aggregate criterion in the PHRM that encapsulates thechange of pool's quality over time.

Minimal Threshold—is an aggregate criterion in the PHRM thatencapsulates minimal actual or predicted pool health that would retainthe same pool without changing pool's exit nodes. It is establishedbased on the parameters established in the user device's request.

Improvement Score (IS)—is an aggregate criterion in the PHRM and theENQRM that encapsulates the difference between predicted pool healthwith and without a particular exit node in the pool. It is used by theENQRM to decide the fitness of an exit node to a particular pool.

Service History—relates a particular user's device with particular exitnodes or pools of exit nodes and once the service ends for the userdevice, service history can be deleted, archived or accumulated but notused.

Aggregated History—includes but is not limited to aggregated data aboutExit Nodes' overall performance their entire history or a period oftime. Some exemplary parameters stored in aggregated history are averagespeed, average response time (globally or to a particular service),average uptime, average schedule (connect and disconnect timesignatures), changes in geographical location, changes in connection andIP type, changes in consent to participate in a distributed CDN accessmodel, variations in which median and percentile groups are used insteadof average values, and others.

Exit Node Pool—is a set of exit nodes that is being actively used for auser's request.

Exclusive Node—is an exit node that only serves a particular user or auser's request and is not shared with other users or requests by thesame user while exclusivity is valid. The exclusivity parameter can besaved in the service history of the History Database.

Exclusive Pool—is a pool that contains only exclusive exit nodes. Theexclusivity parameter is saved in the service history while in use andmight be removed, archived or kept unused when a user stops using theservice.

Static Parameter—is a parameter that has a fixed value over a period oftime, such as exit node's geographical location, connection and IP type,consent to participate in a distributed CDN access model, and others.

Dynamic Parameter—is a parameter that constantly changes in time, suchas current and average speed, traffic per day or overall, sessionruntime, battery life, variations in which median and percentile groupsare used instead of average values, and others.

Batch—is a set of exit nodes that return a test response within apredefined portion of time, e.g. one second or similar.

Heuristic Prediction—is a prediction of the development of a set ofparameters or a parameter that is based on the history of the change ofthese parameters and the current values. It informs decisions whichthemselves influence the development of the parameters.

Candidate Exit Nodes—is the set of exclusive or non-exclusive exit nodesthat are available for use by the user's device but are not involved inthe active rotation.

User Device 104—is any device or devices used by the user to communicatetheir requests to the Service Provider Infrastructure 120. User Device104 can be a singular device, like a computer or a tablet, or it can bea network of devices connected together.

Exit Node Connection Kit—is a collection of software functions in oneinstallable package deployed in the Exit Nodes 102 that managesconnectivity with the Gateway 106 and as long as the Exit NodeConnection Kit is engaged, the communication is possible. If the ExitNode Connection Kit is stopped, then an exit node is inactive and unableto interact with the Service Provider Infrastructure 120. Exit NodeConnection Kit can be a standalone application, an integrated componentof another application, a system service, a launched daemon, and others.Exit Node Connection Kit can be executed from any Computer ReadableMedium 806.

In one aspect, one of the embodiments described herein provides systemsand methods for effectively selecting and managing Exit Nodes 102 bymaking them exclusive for one User Device 104. Once a User Device 104registers with Gateway 106, its information and requested parameters arestored in the User Database 108 and whatever pool is ascribed to executeits request, the pool can become exclusive to the User Device 104, thusmaking the Exit Nodes 102 contained in the pool unavailable to executerequests of other Users. The relation between the User's Device 104 andthe exit node pool is recorded in the History Database 114 by the LogicUnit 112. This mechanism ensures that the same User Device 104 workswith the same pool of exclusive exit nodes whenever they are available.This maximizes the efficiency of the Exit Nodes' 102 usage because newexit nodes are only required when an exclusive pool is exhausted. Byusing this mechanism the service provider is able to book the lowestsufficient number of exit nodes and fulfill the needs of its users.

In another aspect, one of the embodiments described herein providessystems and methods for effectively testing and ranking Exit Nodes 102and heuristically predicting their future performance by aggregatingtheir history and the current and future needs of the pools assigned toUser's Device 104. All connected Exit Nodes 102 report to the Gateway106 by responding to test requests sent by it. The Gateway 106 cancollect the test responses in a batch that contains all the responsesreceived in a defined period of time. The Gateway 106 reports the testresults that contain information about Exit Nodes' 102 currentperformance to the Logic Unit 112 to produce aggregated data about theExit Nodes 102 and store them in the History Database 114. The LogicUnit 112 then uses the History Database 114 to make heuristicpredictions about the future performance of the currently active poolsas well as the newly connected exit nodes.

The prediction is facilitated by mechanisms that evaluate both thegeneral pool health and the quality of a particular exit node and itspotential impact on the pool health. One such exemplary mechanismpredicts that the pool health will fall below the minimal acceptablepool health threshold at a certain time and instructs the Logic Unit 112to request new exit nodes for that pool. Logic Unit 112 then engageswith another mechanism that determines which exit nodes would make thebest fit for the pools currently requesting expansion based on thepredicted improvement score that they would achieve. These mechanismscan be different platform components from Logic Unit 112 or a distinctfunctionality within Logic Unit 112. In what follows, the mechanisms aretreated as functionalities of the Logic Unit 112 but they could beperformed outside of it without changing the overall functioning of theembodiments.

In yet another aspect, at least one of the embodiments described hereinprovides systems and methods for effectively forming pools of Exit Nodes102 based on their aggregated history and heuristically predictedperformance as well as fitness for a User Device 104 request. UserDevice 104 requested parameters are evaluated against the aggregatedhistory of Exit Nodes 102 and a heuristic prediction is made as to thefuture pool health, so the Logic Unit 112 can choose the optimallyformed pool that will be predictably healthy and further predictionswill be made to maintain the optimally curated pool. If the predictedexclusive pool is exhausted, a new exit node is also selected usingheuristic prediction, so that the pool health is maintained above theminimal acceptable pool health threshold.

Heuristic prediction is generally performed within the server thatcontains Logic Unit 112, and, in some embodiments, may require aconfiguration file or multiple files, although the heuristic predictioncan be performed on a different server, e.g., a 3rd party service ordata processing platform. Heuristic prediction requires previous datawhich is aggregated over a period of time. Types of data that can beused in heuristic prediction include frequency, intervals, and scheduleat which static parameters are changed (geolocation of exit nodes, theirIP type, consent to participate in CDN proxying, and others), dynamicparameters (time seen, session duration and timestamps, timestamps ofidleness, current total traffic, traffic per day or other period oftime, response time, latency, target, battery life, and others), andaggregated dynamic parameters over any period of time (average speed,average session duration and timestamps, average traffic, averageresponse time, average latency, most/least visited targets, error ratewith a particular target, variations in which median and percentilegroups are used instead of average values, and others) in anycombination and with any weights associated with the parameters.

In at least one embodiment, Gateway 106, User Database 108, Exit NodeDatabase 110, Logic Unit 112, History Database 114, and Testing Module118 are parts of the Service Provider Infrastructure 120.

FIG. 1 shows an exemplary overall structure that comprises a User Device104, which can be any computing device (e.g., a personal computer,mobile phone, a tablet computer) having access to a particular network(e.g. Internet connection), a Service Provider Infrastructure 120,containing Gateway 106, User Database 108, Exit Node Database 110, LogicUnit 112, History Database 114, and Testing Module 118, and Exit Nodes102. While the elements shown in the FIG. 1 implement an exemplaryembodiment, some elements in other embodiments can have different titlesor can be combined into a single element instead of two separateelements (for example, Exit Node Database 110 can be combined withHistory Database 114 as a single infrastructure component). However, thefunctionality of elements and the flow of information between theelements is not impacted generally by such combinations orconsolidations. Therefore, FIG. 1 as shown should be interpreted asexemplary only, and not restrictive or exclusionary of other features,including features discussed in other areas of this disclosure.

Within the Service Provider Infrastructure 120, Gateway 106 communicateswith the outside elements, namely the User Device 104 and the Exit Nodes102. While communicating with the User Device 104 it can accept requestswith a set of parameters from it. These parameters correspond toattributes of the Exit Nodes 102. The Gateway 106 serves as the mediumbetween User Device 104 and Exit Nodes 102 and the rest of ServiceProvider Infrastructure 120 serves to select, maintain, exchange, andotherwise manipulate pools of exit nodes and single exit nodes.

Upon receiving a request from a User Device 104, the parameterscontained in the request Gateway 106 stores the parameters in UserDatabase 108. User Database 108 is basically a storage that containsUser Device 104 identity and the requested technical parameters. When anew request is made, a pool of exit nodes or an exit node is required toserve that request. To acquire the required pool or exit node, Gateway106 makes a request to Logic Unit 112. Logic Unit 112 proceeds to form apool. In at least one embodiment, Logic Unit 112 addresses Exit NodeDatabase 110 to form an initial group that corresponds to a predefinedset of static criteria. Exit Node Database 110 already contains basicinformation about an exit node, e.g. its geographical location, IP type,and consent to participate in a distributed CDN access model. If suchparameters are stored in User Database 108, Logic Unit 112 will specifythem in the request to Exit Node Database 110. This way only eligibleexit nodes will be included in the initial group. A set of these initialstatic parameters defines the necessary criteria for an exit node toenter the pool.

In one embodiment, once Logic Unit 112 acquires an initial group ofeligible exit nodes, it consults History Database 114, and in particularservice history, to check if the exit nodes in the initial group are notexclusive to any other User Device 104. If they are, it removes themfrom consideration and requests new exit nodes from Exit Node Database110. In some embodiments Exit Node Database 110 and History Database 114might be consolidated into one mechanism, so Logic Unit 112 couldrequest only eligible and non-exclusive exit nodes in one request. OnceLogic Unit 112 forms a pool that is both eligible and non exclusive, itforwards the pool to Gateway 106 and records the pool as exclusive toUser Device 104 in User Database 108. Gateway 106 can then begin toserve the pool to User Device 104. Serving a pool refers to the actionby which Gateway 106 enables User Device 104 request to be executed bythe Exit Nodes 102 individually or in a pool. If an exit node or a poolof exit nodes is being actively used to perform a user's requests (likecontent fetching from a target website, scraping, media streaming, andothers), then that exit node or pool is being assigned to that userwhich places the request.

In one embodiment, when Logic Unit 112 forms the initial group, it teststhe eligibility and non-exclusiveness of the exit nodes but does notneed to perform any testing in Testing Module 118. Instead, it addressesHistory Database 114, and in particular aggregated history, to make aheuristic prediction about the pool through PHRM, so that pool's healthis already calculated at the start of the service. When a pool iscreated based on the heuristic prediction, it is forwarded to Gateway106 and recorded as exclusive to User Device 104 in User Database 108.Gateway 106 can then begin to serve the pool to User Device 104.

A pool that is being assigned has to be constantly monitored, measuredand maintained, otherwise the quality of service might be compromised.It is one of the responsibilities of the service provider to maintainthe quality of service. Thus, tests are being performed on the exitnodes. Generally, there are two types of tests available to the serviceprovider—organic and synthetic. Organic tests are monitoring realuser-server activities. Synthetic tests are imitating such activities.For example, synthetic tests could generate human-like scraping orstreaming requests and run them against real targets, later measuringand analyzing the response. In what follows, organic and inorganic(synthetic) tests are not distinguished therebetween, since both typesof tests can be equally performed without changing the overallfunctioning of the embodiments.

Some tests can consist of Exit Nodes 102 sending requests to a target(for example, to a testing service or a specific target website) andreturning the results to Gateway 106. The results are then processed byLogic Unit 112 and stored in History Database 114, more specificallyaggregated history. The results can be kept for an indeterminate periodof time or used just for a particular prediction. Tests can also beexecuted by the service provider. In that case Logic Unit 112 makes arequest to Testing Module 118 to perform a test to determine aparticular quality of an exit node. Testing Module 118 performs thetesting through Gateway 106. The results of tests are returned to theLogic Unit 112 to be aggregated and then recorded in History Database114, aggregated history.

In one embodiment, one of the functions of Logic Unit 112 is to predictthe pool health of every pool through PHRM. If Logic Unit 112 determinesthat the pool health will traverse the minimal acceptable pool healththreshold in the pre-defined period of time in the future, it replacessome of the pool's exit nodes in advance, so that the Pool healththreshold is not trespassed.

Logic Unit 112 uses another mechanism—ENQRM—to determine which pool anexit node would improve the most. It relies on the improvement score todo so, where the Improvement score indicates the assessed measurabledifference in quality between two options—the predicted pool health withthe said exit node included in the pool and with the pool leftunmodified. The pool that would have the biggest improvement from theexit node being added receives the new exit node. If an exit node doesnot improve any pool, it can be reserved for later use and leftidle/vacant. If all pools are sufficiently populated, exit nodes can bereserved or assigned to pools non-exclusively.

There can be multiple types of events or conditions that can triggerreplacing exit nodes within a pool. They include but are not limited toan exit node disconnecting, pool's health dropping or being predicted todrop, a change in the overall quality of the exit nodes, a requestparameter change requested by the user, an overload of theinfrastructure, an increase in either the number or volume of userrequest or exit nodes, or any combination of these events.

There can be different mereological configurations of exit nodes andpools. Mereology defines parthood relationships. The current embodimentsdo not rely on a specific mereological system or ontology. Pools can beordered, unordered, or partially ordered sets or aggregates of exitnodes. The relationship between exit nodes and pools can be defined asreflexive or non-reflexive, transitive or non-transitive, symmetric,asymmetric, or antisymmetric, or any non-contradictory combination ofthese qualities. Pool to pool relations can be defined under anyset-axiomatic principles. None of these conceptions change the overallfunctioning of the embodiments.

FIG. 2 shows a functional diagram of the PHRM that is contained in LogicUnit 112. FIG. 2 represents the general mechanism dedicated tomonitoring and predicting pool health. Pool health is the pool's qualityover time. Quality can be calculated by assigning different values andweights to the parameters formulated by User's Device 104 and registeredin User Database 108. Each pool's quality can be calculated differentlybased on the parameters requested by the user (service speed,reliability, response time, traffic load, schedule, compatibility withthird party services, and others). Minimal acceptable pool healththreshold can be established by the service provider's decisions basedon the user's requirements.

The minimal acceptable pool health threshold can be fixed or it canchange depending on the resources available. Minimal acceptable poolhealth threshold represents the minimal quality that is acceptable for apool to support the quality of service expected. PHRM calculates andheuristically predicts the values of pool health over time. Heuristicpredictions can be based on the aggregated history and/or servicehistory contained in History Database 114 or any other database.Attributes, relevant for prediction in the service history can include:total pools per period, total users (since a user through a device canoperate multiple pools, this differs from “total pools” attribute),total idle connection time, total pool time, average pool time, averagepool change rate, average pool health, and other similar attributes orany combination thereof.

There can be various mathematical and statistical models used forheuristic prediction and optimization. Most models will provide a resultwithin some confidence range but confidence ranges are not necessary.There can be additional steps added to the mechanism due to mathematicalmodels used in optimization (for example, relaxation and approximationmethods) but this fact does not change the overall structure of themechanism or the current embodiments more generally.

Both PHRM and ENQRM can include machine learning algorithms. Machinelearning can be broadly defined as computational methods usingaggregated data to improve performance or to make accurate predictions.Here, aggregated data refers to the past information available to themachine learning algorithm, which typically takes the form of electronicdata collected and made available for analysis. The data made availableto PHRM and ENQRM is the aggregated exit node history contained in theHistory Database 114. Both PHRM and ENQRM can be any heuristic that canbe trained using available data to predict future results. Computationalor machine learning models may be used here for heuristic prediction (orfor a part thereof).

Both PHRM and ENQRM may comprise computational models such as neuralnetworks, classification or regression trees, support vector machines,logistic regressors, Gaussian process models, or other computationalmodels. They essentially decide the suitable coefficients, loads,groupings, associations, boundaries, hyperparameters or other modeltraits that are utilized by the general heuristic prediction to makeforecasts, by feeding as inputs into the heuristic prediction aggregatedhistorical and service data, contained in the History Database 114.

Some computational or machine learning models are supervised, meaningthey are trained for each case on defined training examples with a knownoutput. Other models of computational or machine learning useunsupervised learning, meaning that they are trained by unlabeledexamples that have no defined outputs for each example.

For example, if PHRM and ENQRM are an artificial neural network model(supervised or unsupervised), various coefficients that are used by theneural network have to be learned. The neural network may learn thesecoefficients using the input training data and comparing the outputresult to known actual results (i.e., whether/when pools and exit nodesbeing ranked performed as predicted by PHRM and ENQRM).

The accuracy of the predictions will depend upon the computationalcomplexity of PHRM and ENQRM (how many independentvariables/neurons/neuron-layers/etc. are considered), the breadth ofhistorical data in the History Database 114 used to train the heuristicprediction, and the available data about each exit node. Moreover, sincethe aggregated history is periodically updated, it is not static and mayreceive new data or updates to previously received data. Thus, PHRM andENQRM may be configured to periodically re-train as data is updated.

If PHRM determines that pool health is below the minimal acceptable poolhealth threshold, Logic Unit 112 informs Gateway 106 and it requests newexit nodes to be added to the pool. If pool health is above the minimalacceptable pool health threshold, a pool can be maintained as it is. If,at a given moment, PHRM predicts that pool health will fall below theminimal acceptable pool health threshold in future (say, t2) within apre-defined period of time e.g. next 30 minutes, then Logic Unit 112requests new exit nodes in advance (say, at t1), so that the thresholdwould not be breached. The precise time signatures will depend on themathematical models used which do not specifically impact the overallfunctioning of the mechanism. The difference between the a) predictedquality of the pool without change and b) with the addition of a newexit node or exit nodes, is the improvement score of that exit node.

FIG. 3 represents a functional diagram of ENQRM. When PHRM determinesthat the pool health has violated the minimal acceptable pool healththreshold or is predicted to do so within a pre-defined period of time,Logic Unit 112 informs Gateway 106 and Gateway 106 requests forming anew pool of exit nodes for the request. The new pool can haveoverlapping exit nodes with the previous ones. In other words, only someof the exit nodes in the pool can be chosen to be replaced. Otherwise, afull pool can be replaced at once.

Logic Unit 112 directly obtains candidate exit nodes from Exit NodeDatabase 110 which have to be evaluated by the ENQRM in Logic Unit 112.The selection of candidate exit nodes can be gathered based on theattributes of the exit nodes, some kind of order among them (forexample, the order in which they register with Gateway 106), they can begrouped into batches based on a time interval in which they performtests, or any such criteria. ENQRM evaluates the suitability of eachexit node to a particular pool according to user requests parameters andthe attributes of the exit node. That calculation is based on theimprovement score of each exit node and the corresponding pool. ENQRMcan consider pools for optimization changes that are currentlyconsidered for new exit nodes (i.e. they are below the minimalacceptable pool health threshold or are expected to fall below it) or itcan choose to evaluate all exit nodes for general improvements of all orany or some pools.

For example, given an exit node and six pools, ENQRM calculates theimprovement score of the exit node in each pool and determines thehighest improvement score the exit node can deliver. The differencesamong improvement scores are the result of the current or predicted poolhealth and an exit node's current or predicted performance. In oneembodiment, if only a single exit node is evaluated, it is immediatelyassigned to the pool with the highest assessed improvement score. Ifthere are multiple exit nodes evaluated as a batch, the distributiondecision is made after all exit nodes are evaluated. The exit nodes areassigned to the pools in which they have the highest improvement score.If the same exit node has the highest score in more than one pool, itcan be assigned to the pool in which it has a higher improvement score.

It is unlikely that improvement scores will be equal for any exit nodesor pools because of the complex initial conditions and multiple factorstaken into account while calculating the improvement scores. However, anadditional step can be devised that enacts priority rules based on theweight of parameters (say, predicted speed over predicted sessionlength), the priority based on pool size, or other such criteria. TheENQRM can also be instructed to wait for the next round of tests if theresults are undetermined. However, such additional steps do not changethe overall functioning of the ENQRM or any of the embodiments. If anexit node has negative values calculated as its improvement score withall pools, it can be left unassigned to any pool. Negative values ofimprovement score means that an exit node would decrease pool healthinstead of increasing it.

FIG. 4A represents an exemplary flow diagram of exit node rotationwithout heuristic prediction. In step 401, Exit Nodes 102 register withGateway 106. Step 401 might involve additional steps initiated by theowner of the Exit Node's 102, like providing identification, reviewingand accepting terms of service, and similar actions. In step 403,Gateway 106 registers exit node's initial static data. Static dataincludes but is not limited to the acceptance of the terms of services,device type and model, operating system type and version, identification(e.g. an e-mail address), and consent to participate in a distributedCDN access model. There can be different levels of consent that specifythe type of activities that an exit node's owner agrees to relay throughtheir device. In step 405, User's Device 104 makes a request. A requestcan depend on the service type. It can be optimized for a certainactivity, like scraping or streaming. A user can be given tools tomanually define their request and assign parameters to it.

In step 407, Gateway 106 registers the user's requested parameters toUser Database 108. There can be multiple sets of parameters per user ifa single user registers multiple requests. The parameters can be pre-setby the service provider or they can be manually determined by the user.Generally, the parameters are first set in an SLA between the serviceprovider and the user in the form of business requirements, for example,agreed service targets, criteria for target fulfilment evaluation, rolesand responsibilities of the service provider, duration, scope andrenewal of the SLA contract, supporting processes, limitations,exclusions and deviations, and similar clauses.

However, SLA information is translated into measurable technicalparameters usable within Service Provider Infrastructure 120, forexample, service speed, reliability, response time, traffic load,schedule, compatibility with third party services (like a certain targetwebsite), and others. Request parameters are registered and stored inUser Database 108. In some embodiments, this translation from SLA totechnical parameters can be omitted if the user specifies the parametersdirectly. Steps of registering the information and making a request(steps 401-407) can happen one after another or at the same time. Steps405 and 407 can happen before 401 and 403. This order does not changethe overall functioning of the embodiments.

In step 409, Gateway 106 requests a pool, specified by parametersrecorded in User Database 108 in step 407. Gateway 106 can formulate itsrequest for a pool in such a way that the parameters required by theuser are reflected in that request. Logic Unit 112 receives the requestto form a pool. In step 411, Logic Unit 112 retrieves an initial groupfrom Exit Node Database 110. The initial group is formed according tothe static parameters that define eligibility criteria for the exitnodes to enter the pool. For example, if in step 405 the user hasspecified that they want to access a target from an exit node inAustralia, in step 407 Gateway 106 would specify that only exit nodeswith the geo-location in Australia are eligible, and in step 411 LogicUnit 112 only requests exit nodes with the last known geo-location inAustralia. In other words, Logic Unit 112 requests only eligible exitnodes to form an initial group.

In step 413, Logic Unit 112 checks whether any of the exit nodes in theinitial group are exclusive to existing pools. The exclusivenessrelations between user requests and particular exit nodes or pools ofexit nodes are stored in the service history of History Database 114. Ifany of exit nodes in an initial group are exclusive, these exit nodesare replaced with other eligible exit nodes. In some embodiments, ExitNode Database 110 and History Database 114 might be consolidated into asingle element. In that case steps 411 and 413 could become a singlestep defining the eligibility criteria to include that an exit node mustbe non exclusive to enter the initial group. However, that does notchange the overall functioning of any of the embodiments. Once theinitial group is formed and Logic Unit 112 confirms that all of the exitnodes in the pool are non-exclusive, Logic Unit 112 requests TestingModule 118 to perform tests on the exit nodes in the initial group instep 415. Testing Module 118 can perform the test through the Gateway106.

Step 415 represents a way for the service provider to know the dynamicparameters of the exit nodes at a given time. The tests performed on theinitial group can be initiated by the service provider or constantlyperformed and reported to the service provider by the exit nodes. In thelatter case, the service provider would have to wait until all exitnodes in the pool return their test results. Exemplary dynamicparameters tested by Testing Module 118 in step 415 include but are notlimited to time seen, session duration and timestamps, total traffic,traffic per day or other period of time, response time, latency, target,battery life, and others. If the test results satisfy the parametersspecified in the user request, the pool or a single exit node (dependingon the request) can be assigned to execute the request. If any of theexit nodes fail to satisfy the criteria or the overall quality(aggregated score of the performance of the pool) does not satisfy therequest, steps 411, 413, and 415 can be repeated. The service providercan choose whether to begin serving the request with a subpar qualitypool before, or while improving it, or wait until a pool is formed. Thisis a policy decision that does not change the overall functioning of anyof the embodiments.

In step 417, when a pool is formed, Logic Unit 112 assigns the pool tothe User's request. In step 419, Logic Unit 112 reports a successfulassignment of the pool to Gateway 106. In step 421, Gateway 106 beginsto execute User's Device 104 requests through the selected pool. Asmentioned above, a service provider can choose to begin service evenbefore a pool or exit node is found and improve the pool's health duringthe service. In step 423, Logic Unit 112 can choose to make the exitnodes in that pool exclusive to that user or user's request. If this isdone, then the exit nodes in this pool will not be used for another useror a request by the same user.

FIG. 4B represents the continuation of the exemplary flow diagram ofexit node rotation without heuristic prediction. It continues with step425 in which a trigger is executed for Gateway 106 to find new exitnodes for populating the active pool. In one embodiment, the trigger canbe one of the exit nodes in the pool disconnecting thus lowering thepool health. This case is represented by step 425 in which one or moreof the Exit Nodes 102 disconnect. However, the trigger can be definedmore broadly, including, but not limited, cases of a drop in exit nodes'speed, growing latency, change in geolocation or IP type, and similarevents. The trigger can originate from within Service ProviderInfrastructure 120 or outside of it. The type of trigger does not changethe overall functioning of any of the embodiments.

Once the trigger is activated, in step 427, Gateway 106 makes a requestto Logic Unit 112 for a new exit node or multiple exit nodes. Step 429represents the first way that Logic Unit 112 would look for exit nodes,i.e. it checks History Database 114, and more specifically servicehistory for exclusive exit nodes, i.e. nodes that are exclusive to theuser or a particular request. These exit nodes have the explicitadvantage over others because they have been tested for that particularrequest before and thus are already fit for executing the request. IfLogic Unit 112 finds available exclusive nodes, it immediately returnsthem to Gateway 106 to be served. Such a rotation of the same exit nodesfor the same user allows the service provider to effectively manage theusage of the exit nodes.

However, if in step 429 Logic Unit 112 does not find exclusive exitnodes, it must acquire new exit nodes. In step 431, Gateway 106 requestscandidates to add to the current pool according to parameters recordedin User Database 108 in step 407. Gateway 106 can formulate its requestfor candidate exit nodes in such a way that the parameters required bythe user are reflected in that request. Logic Unit 112 can request onlyeligible candidate exit nodes.

In step 433, Logic Unit 112 checks whether any of the candidate exitnodes are exclusive to existing pools. The exclusiveness relationsbetween user requests and particular exit nodes or pools of exit nodesmay be stored in the service history of History Database 114 or anyother available database. If any of the candidate exit nodes areexclusive, these exit nodes are replaced with other eligible exit nodes.In some embodiments, Exit Node Database 110 and History Database 114might be consolidated into a single element. In that case steps 431 and433 could become a single step defining the eligibility criteria toinclude that an exit node must be non-exclusive to enter the initialgroup. However, that does not change the overall functioning of any ofthe embodiments. Once Logic Unit 112 confirms that all of the candidateexit nodes are non-exclusive, Logic Unit 112 requests Testing Module 118to perform tests on the candidate exit nodes through the Gateway 106 instep 435.

Step 435 represents a way for the service provider to know the dynamicparameters of candidate exit nodes at a given time. The tests can beinitiated by the service provider or constantly performed and reportedto the service provider by the exit nodes. In the latter case, theservice provider would have to wait until all exit nodes in the poolreturn their organic test results. Exemplary dynamic parameters testedby Testing Module 118 in step 435 include but are not limited to timeseen, session duration and timestamps, total traffic, traffic per day orother period of time, response time, latency, target, and others. If thetest results satisfy the parameters specified in the user request,candidate exit node(s) can be added to the current pool as presented instep 437. In step 439, Logic Unit 112 can choose to assign the updatedpool as exclusive, thus making the newly added exit node exclusive tothe user or user's request. If Logic Unit 112 assigns the new pool asexclusive and the disconnected exit node in step 425 was also exclusive,then the overall exclusive pool of the user or user's request isenlarged.

In one of the embodiments, the Logic Unit 112 can also decide to makethe failed or disconnected exit node no longer exclusive to that pool.If Logic Unit 112 decides to assign the updated pool to exclusive, itmay store that data in the service history of History Database 114 orany other database.

If any of the exit nodes in the pool disconnect again or any othercondition triggers Gateway 106 to request new exit nodes, the sameprocedure starting with step 425 and potentially ending with step 439commences. However, every time when in step 439 Gateway 106 assigns theupdated pool to exclusive, the overall number of exclusive exit nodesincreases, so the likelihood that an available exclusive exit will befound increases.

In at least one of the embodiments, if the user ends service agreementor otherwise ceases to use the service, the service history in HistoryDatabase 114 can be deleted, archived or accumulated but not used andall the exit nodes are released from their exclusive bond with a user ora user's request. However, even if particular service data can bedeleted, archived or disused, aggregated data can be extracted from theservice data and stored within the aggregated history of HistoryDatabase 114. Examples of such data include the time period for whichthe exit node has been exclusive, the total number of exclusive requestsit has executed, and similar data.

FIG. 5 represents yet another embodiment of the exemplary flow diagramof exit node rating with heuristic rating prediction. In someembodiments, the heuristic rating procedure can be triggered by certainfactors or a number of factors, like a drop in speed, exit nodedisconnection and similar events. However, in this embodiment, ratingprocedure does not need to be triggered. It is an ongoing process thatsupervises the quality of the exit nodes and the pool health of exitnode pools. Instead of being triggered, it can itself trigger proceduresfor pool maintenance.

In step 501, Gateway 106 tests Exit Nodes' network connectivity bytesting the Exit Nodes 102 and reporting the results. This testing is anongoing scheduled activity. Testing means that Gateway 106 is sendingexit nodes small packets of data and is receiving responses that arebeing measured. Additionally, Gateway 106 monitors and measures theorganic activity of the exit nodes when they execute requests. Lastly,Gateway 106 can initiate testing in which it makes requests through theexit nodes to a target. The target can be a specific target website, aspeed testing service server or similar destinations. Measuredinformation in the responses contains data about the round-trip time formessages between the exit node and the destination (be it Gateway 106 ora target). Tests are generally executed once in a pre-set period oftime, for example a minute for every exit node. In step 503, in someembodiments, Gateway 106 can decide to immediately forward individualresults to Logic Unit 112 to process. However, in one embodiment,Gateway 106 can wait a pre-set period of time to collect the results andreturn them in a batch, thus saving processing time and cutting downrepeated procedures. The collection time period can be pre-set, forexample once a second, or it can be triggered once a certain number oftest responses is collected, or both of these conditions can be applied.

Gateway 106 can also request for additional tests to be done on the exitnodes by Testing Module 118. Testing Module 118 can additionally learnabout an exit nodes' current performance by testing dynamic parameters,time seen, session duration and timestamps, total traffic, traffic perday or other period of time, response time, latency, targets, batterylife, and others. Testing Module 118 can perform the tests through theGateway 106.

In some embodiments, Exit Nodes 102 can already be instructed to includethat information in the initial test reports in step 501. In the lattercase, tests by Testing Module 118 would not be needed and step 505 wouldbe omitted but testing would need to be done internally in the ExitNodes 102. In either case, in step 507, Gateway 106 reports the resultsto the Logic Unit 112.

In step 508, Logic Unit 112 gathers the aggregated data from the tests,monitoring organic requests and analysis of the tests results and storesit in the aggregated history of History Database 114. The aggregation ofdata can be done in different data models, including but not limited toan entity-relationship model, relational model, record-based logicalmodel, hierarchical model, object-oriented model, object-relationalmodel, flat model, semi-structured model, associative model, contextmodel, and others.

In step 511, Logic Unit 112 engages in the two predictivemechanisms—PHRM and ENQRM described above and visualized in FIG. 2 andFIG. 3, respectively. Generally, Logic Unit 112 (by engaging with PHRMand ENQRM) relies on the aggregated data in History Database 114 toheuristically predict the future behavior, or status, of exit nodes inthe pool and determine the pool health. It can also determine andpredict the quality of particular exit nodes that could becomecandidates to pools that need exit nodes to be replaced. Pool health andexit node quality are attributes that are used to determine if exitnodes need to be replaced and which exit node should go to which pool.These attributes and predictions are stored in Exit Node Database 110 byLogic Unit 112 until a decision is made. In step 513, pool healthinformation is stored by Logic Unit 112 in Exit Node Database 110. Instep 515, exit node quality information is stored by Logic Unit 112 inExit Node Database 110.

The process described in FIG. 5 is an ongoing process in ServiceProvider Infrastructure 120. It does not need to begin or end inspecific conditions and in some embodiments it is constantly performed.It is an evaluation mechanism that monitors and supervises the workflowof the service. However, by itself it does not make any changes to theservice. In other words, it is descriptive i.e. it describes theperformance of the exit nodes and pools and predicts their futurebehavior. It then makes conclusions about pool health and exit nodequality but it does not enforce any decisions about how the service hasto be run. It provides the basis or foundation for making decisions butdoes not make those decisions by itself. It informs the decisions ofother elements in the Service Provider Infrastructure 120, in particularLogic Unit 112 and Gateway 106.

An exemplary process in which such descriptions would be used to makedecisions about pools and exit nodes is contained in FIG. 6A and FIG.6B. FIG. 6A represents an exemplary flow diagram of exit node rotationwith heuristic prediction. In step 601, Exit Nodes 102 registers withGateway 106. Step 601 can optionally involve additional steps on theExit Nodes 102 owner's side, like providing identification, reviewingand accepting terms of service, and similar actions.

In step 603, Gateway 106 registers exit node's initial static data. Thisincludes but is not limited to the acceptance of the terms of services,device type and model, operating system type and version, identification(like an e-mail address), and consent to participate in a distributedCDN access model. There can be different levels of consent that specifythe type of activities that an exit node's owner agrees to relay throughtheir device. In step 605, User's Device 104 makes a request. A requestcan depend on the service type. It can be optimized for a certainactivity, like scraping or streaming. A user can be given tools tomanually define their request and assign parameters to it.

In step 607, Gateway 106 registers the user's requested parameters toUser Database 108. The parameters can be pre-set by the service provideror they can be manually determined by the user. Generally, theparameters are first set in an SLA between the service provider and theuser in the form of business requirements, for example, agreed servicetargets, criteria for target fulfilment evaluation, roles andresponsibilities of the service provider, duration, scope and renewal ofthe SLA contract, supporting processes, limitations, exclusions anddeviations, and similar clauses. However, SLA information is translatedinto measurable technical parameters usable within Service ProviderInfrastructure 120, for example, service speed, reliability, responsetime, traffic load, schedule, compatibility with third party services(like a certain target web site), and others. Request parameters areregistered and stored in User Database 108. In some embodiments, thistranslation from SLA to technical parameters can be omitted if the userspecifies the parameters directly. Steps 601, 603, 605, 607 can happenone after another or at the same time. Steps 605 and 607 can happenbefore or after 601 and 603. The reordering of these steps does notchange the overall functioning of the embodiments.

In step 609, Gateway 106 requests a pool to execute the user's request,specified by parameters recorded in 109 in step 607. Gateway 106 canformulate its request for a pool in such a way that the parametersrequired by the user are reflected in that request. Logic Unit 112receives the request to form a pool. In step 611, Logic Unit 112retrieves an initial group from Exit Node Database 110. The initialgroup is characterized by the static parameters that define eligibilitycriteria for the exit nodes to enter the pool. For example, if in step605 the user's device has specified that it wants to access a targetfrom an exit node in Australia, in step 607 Gateway 106 would specifythat only exit nodes with the geo-location in Australia are eligible,and in step 611 Logic Unit 112 only requests exit nodes with the lastknown geo-location in Australia.

In other words, Logic Unit 112 requests only eligible exit nodes to forman initial group. In step 613, Logic Unit 112 checks whether any of theexit nodes in an initial group are exclusive to existing pools. Theexclusiveness relations between user requests and pools are stored inthe service history of History Database 114. If any of exit nodes in aninitial group are exclusive, these exit nodes are replaced with othereligible exit nodes. In some embodiments, Exit Node Database 110 andHistory Database 114 might be consolidated into a single element. Inthat case steps 611 and 613 could become a single step defining theeligibility criteria to include that an exit node must be non exclusiveto enter the initial group. However, that does not change the overallfunctioning of any of the embodiments.

In step 615 the current embodiment differs from the one represented inFIGS. 4A and 4B. Instead of testing the pool for dynamic parameters, asrepresented in step 415, this embodiment performs predictive ranking ofthe exit nodes to form the pool for the user's request. Morespecifically, Logic Unit 112 engages in the two mechanisms—PHRM andENQRM—to determine which exit nodes forming a pool would satisfy theminimal acceptable pool health threshold pre-set by the user's requestor the internal decision by the service provider. Given that PHRM andENQRM are heuristic predictive mechanisms, it allows Logic Unit 112 tonot only form an optimal pool at the moment but also predict its futurebehavior.

Step 615 can contain a flow of actions that is detailed in FIG. 5 (steps501-515) which consists of gathering data, aggregating data, recordingdata in the History Database 114, performing heuristic prediction basedon the aggregated data, and recording the prediction results.

For example, when Logic Unit 112 forms the initial group, it predictsthe future behavior of its exit nodes, including their activitytimestamps. Thus, even if a particular exit node, according to theaggregated data, satisfies the parameters pre-set by the user's requestbut it approaches its usual disconnect time, its quality can be rankedlower than another exit node that is inferior in other attributes but ispredicted to stay connected for a longer time. In another example, LogicUnit 112 might not choose an available exit node despite its gooddynamic parameters because in the past it has changed its IP type often(e.g., from mobile to Wi-Fi and back) and the user request specifiesmobile only or Wi-Fi only. In other words, Logic Unit 112 predictspotential violations of the minimal acceptable pool health threshold ifa certain exit node is included and heuristically learns from it, i.e.it does not include that exit node in the pool or replaces the exit nodewith another one.

Once the initial group satisfies the minimal acceptable pool healththreshold, Logic Unit 112 can choose to assign the pool to the user instep 615 and report success to Gateway 106 in step 617. A serviceprovider may choose to a) begin service before a pool is fully formedand b) improve the pool's health during the service. In step 623, LogicUnit 112 can choose to make the exit nodes in that pool exclusive tothat user or user's request. If this is done, then the exit nodes inthis pool will not be served to another user or a request by the sameuser.

FIG. 6B represents the continuation of the exemplary flow diagram ofexit node rotation with heuristic prediction. It continues with step 625in which Logic Unit 112 predicts a violation of the minimal acceptablepool health threshold according to the PHRM. In other words, Logic Unit112 predicts at t1 that a violation will occur at t2 and identifies theexit nodes with the lowest quality rating at t2. They will likely be thecause of the fall in the pool's health. Logic Unit 112 reports thisprediction to Gateway 106. In step 627, Gateway 106 sends a request toLogic Unit 112 to replace the exit nodes. In step 629, Logic Unit 112first checks whether exclusive exit nodes are available. If they are,they can be used to replace the current exit nodes with the lowestquality rating. However, if exclusive exit nodes are not found, theymust be obtained from the set of all available and eligible exit nodes.

In step 631, Logic Unit 112 obtains candidate exit nodes. Candidate exitnodes are eligible exit nodes and when Logic Unit 112 makes a requestfor candidates to Exit Node Database 110, it can already specifycriteria for exit node eligibility. In step 633, Logic Unit 112 checkswhether any of the candidate exit nodes are exclusive to existing pools.If they are, they are discarded as candidates. In step 635, Logic Unit112 engages in the ENQRM to determine which exit nodes would fit inwhich pools the best. In some embodiments, pool's request to replaceexit nodes can be handled in succession, meaning that pools withpredicted minimal acceptable pool health threshold violations would havetheir exit nodes replaced on a first-come first-served basis. However,such a procedure does not make sure that the exit nodes go to the mostfitting pools. Thus, in one embodiment, all the pools requestingreplacement are grouped and evaluated together.

In the latter case, step 635 would be delayed until batch results arereturned (for example, batch delay time can be set at 1 second or anyother interval by the service provider). Once batch results arereturned, Logic Unit 112 has access to a batch of available candidateexit nodes and pools requesting exit node replacement. Logic Unit 112then uses ENQRM to determine the improvement score of each exit node ineach pool. For example, given every exit node in the batch and six poolsthat are predicted to violate the minimal acceptable pool healththreshold, ENQRM calculates the improvement score of every exit node ineach pool and determines the improvement score. The differences amongimprovement scores are the result of the current quality and compositionof the pools, the parameters specified by User's Device 104, and everyexit node's predicted performance. In one embodiment, the decision toinclude a particular exit node in a particular pool is made after allexit nodes are evaluated for all pools.

Step 635 can contain a flow of actions that is detailed in FIG. 5 (steps501-515) which includes gathering data, aggregating data, recording datain the History Database 114, performing heuristic prediction based onthe aggregated data, and recording the prediction results.

Once Logic Unit 112 decides which exit nodes improve which pools themost, it makes the decision to assign these exit nodes to these pools instep 637. The exit nodes are assigned to the pools in which they havethe highest improvement score. If the same exit node has the highestscore in more than one pool, it can be assigned to the pool in which ithas a higher improvement score. It is extremely unlikely thatimprovement scores will be equal for any exit nodes or pools because ofthe complex initial conditions. However, an additional step can bedevised that enacts priority rules based on the weight of attributes(e.g., predicted speed over predicted session length), the prioritybased on pool size, or other such criteria. The ENQRM can also beinstructed to wait for the next round of test period if the results areundetermined. However, such additional steps do not change the overallfunctioning of the ENQRM or any of the embodiments more generally. If anexit node has negative values of its improvement score with all pools,it can be left unassigned to any pool. Negative values of improvementscore means that an exit node would decrease pool health instead ofincreasing it.

FIG. 7 represents an exemplary flow diagram of gateway response to anexit node's barriers. In step 701, an exit node connects to the Gateway106 by engaging the Exit Node Connection Kit contained in the exit node.Gateway 106 receives an exit node's request to connect to the ServiceProvider Infrastructure 120. In step 703, Gateway 106 checks criteria inthe Exit Node Database 110 corresponding to the exit node that is tryingto connect. For example, it can check such parameters as consent toparticipate in a distributed CDN access model, IP type, number of exitnodes with the same IP, registration status (active or disabled orbanned), geolocation, and similar. A service provider can decide thatsome or all or any of these parameters are prohibiting factors, orbarriers for connection to the Service Provider Infrastructure 120. Forexample, a service provider can decide that exit nodes from a particularcountry are not allowed to connect. In that case, after detecting thatan exit node is from that country, in step 709, Gateway 106 sends aresponse message to the exit node (more specifically, to the Exit NodeConnection Kit) with the instruction to disable connectivity to theService Provider Infrastructure 120 for a certain amount of time orindeterminately.

The response types sent by Gateway 106 to an exit node's Exit NodeConnection Kit can be instructions to continue working, completelydisable the Exit Node Connection Kit, temporarily disable Exit NodeConnection Kit's activity, disable Exit Node Connection Kit's activityuntil further requested or similar instructions. In at least one of theembodiments, a response (step 709) can be sent immediately afterchecking the registration data (step 703). In other words, if a barrierto entry is detected earlier in the action flow, intermediary steps canbe omitted to save testing activities.

The order in which the barriers are tested can also be important. Forexample, Gateway 106 can choose to test the geolocation, then theconsent to participate in a distributed CDN access model, then the IPtype of the exit node and send a response (step 709) after anyviolations of the barriers without checking the rest of them.

In at least one embodiment, if all of the barriers are passed during theregistration phase in step 703, then, in step 705, Gateway 106 addressesTesting Module 118 to perform tests on the dynamic features ormeasurements of the exit node's performance, such as response time,latency, bandwidth, battery life, or any combination thereof. Serviceprovider can decide that some or all or any of the dynamic parametersalso constitute a barrier for entry and thus send a response to an exitnode to continue working, completely disable the Exit Node ConnectionKit, temporarily disable the Exit Node Connection Kit's activity,disable Exit Node Connection Kit's activity until further requested orsimilar instructions. For example, a service provider can decide that itwill not accept exit nodes with less than 5% battery life or with lessthan 1 mbps (megabytes per second) bandwidth. In that case, if suchbarriers are not met, in step 707, Gateway 106 sends a response withinstructions to completely disable the Exit Node Connection Kit,temporarily disable Exit Node Connection Kit's activity, disable ExitNode Connection Kit's activity until further requested, or similarinstructions.

If an exit node fulfills all barriers, Gateway 106 sends a positiveresponse to the Exit Node Connection Kit to allow further connectivityto the Service Provider Infrastructure 120. A positive response fromGateway 106 allows to execute the Exit Node Connection Kit'sconnectivity functions and to participate in exit node pools, serviceuser's requests and similar functions.

In step 709, Gateway 106 records the results it gathered about an exitnode in Exit Node Database 110, including an exit node's registrationdata (consent to participate in a distributed CDN access model, IP type,number of exit nodes with the same IP, registration status (active ordisabled or banned), geolocation, and similar) and test results(response time, latency, bandwidth, battery life, and others). Step 709is enacted despite the type of instructions sent by Gateway 106 to theExit Node Connection Kit's. More specifically, if a negative response isalready sent in step 703, then testing of dynamic attribute is skipped(step 705) and response is sent immediately (707) and after that orsimultaneously the results are recorded by Gateway 106 in Exit NodeDatabase 114.

When Gateway 106 sends a response with instructions to disable Exit NodeConnection Kit or any of its functionality, the response can containinstructions about how long the cooldown period should be. Cooldownperiod signifies the period during which the Exit Node Connection Kit isnot allowed to connect to the Service Provider Infrastructure 120. ExitNode Connection Kit can be scheduled to periodically retry connectionand initiate the flow of actions, represented in FIG. 7 or it can bedesigned to only do so when the cooldown period has elapsed.

The embodiments herein may be combined in a variety of ways as a matterof design choice. Accordingly, the features and aspects herein are notintended to be limited to any particular embodiment. Furthermore, theembodiments can take the form of hardware, firmware, software, and/orcombinations thereof. In one embodiment, such software includes but isnot limited to firmware, resident software, microcode, etc. FIG. 8illustrates a computing system 800 in which a computer readable medium806 may provide instructions for performing any of the methods andprocesses disclosed herein.

Furthermore, some aspects of the embodiments herein can take the form ofa computer program product accessible from the computer readable medium806 to provide program code for use by or in connection with a computeror any instruction execution system. For the purposes of thisdescription, the computer readable medium 806 can be any apparatus thatcan tangibly store the program code for use by or in connection with theinstruction execution system, apparatus, or device, including thecomputing system 800.

The computer readable medium 806 can be any tangible electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system(or apparatus or device). Some examples of a computer readable medium806 include solid state memories, magnetic tapes, removable computerdiskettes, random access memories (RAM), read-only memories (ROM),magnetic disks, and optical disks. Some examples of optical disksinclude read only compact disks (CD-ROM), read/write compact disks(CD-R/W), and digital versatile disks (DVD).

The computing system 800 can include one or more processors 802 coupleddirectly or indirectly to memory 808 through a system bus 810. Thememory 808 can include local memory employed during actual execution ofthe program code, bulk storage, and/or cache memories, which providetemporary storage of at least some of the program code in order toreduce the number of times the code is retrieved from bulk storageduring execution.

Input/output (I/O) devices 804 (including but not limited to keyboards,displays, pointing devices, I/O interfaces, etc.) can be coupled to thecomputing system 800 either directly or through intervening I/Ocontrollers. Network adapters may also be coupled to the computingsystem 800 to enable the computing system 800 to couple to other dataprocessing systems, such as through host systems interfaces 812,printers, and/or or storage devices through intervening private orpublic networks. Modems, cable modems, and Ethernet cards are justexamples of network adapter types.

Although several embodiments have been described, one of ordinary skillin the art will appreciate that various modifications and changes can bemade without departing from the scope of the embodiments detailedherein. Accordingly, the specification and figures are to be regarded inan illustrative rather than a restrictive sense, and all suchmodifications are intended to be included within the scope of thepresent teachings. The benefits, advantages, solutions to problems, andany element(s) that may cause any benefit, advantage, or solution tooccur or become more pronounced are not to be construed as a critical,required, or essential features or elements of any or all the claims.The invention is defined solely by the appended claims including anyamendments made during the pendency of this application and allequivalents of those claims as issued.

Moreover, in this document, relational terms such as first and second,and the like may be used solely to distinguish one entity or action fromanother entity or action without necessarily requiring or implying anyactual such relationship or order between such entities or actions. Theterms “comprises”, “comprising”, “has”, “having”, “includes”,“including”, “contains”, “containing” or any other variation thereof,are intended to cover a non-exclusive inclusion, such that a process,method, article, or apparatus that comprises, has, includes, contains alist of elements does not include only those elements but may includeother elements not expressly listed or inherent to such process, method,article, or apparatus. An element preceded by “comprises . . . a”, “has. . . a”, “includes . . . a”, “contains . . . a” does not, withoutadditional constraints, preclude the existence of additional identicalelements in the process, method, article, and/or apparatus thatcomprises, has, includes, and/or contains the element. The terms “a” and“an” are defined as one or more unless explicitly stated otherwiseherein. The terms “approximately”, “about” or any other version thereof,are defined as being close to as understood by one of ordinary skill inthe art. A device or structure that is “configured” in a certain way isconfigured in at least that way, but may also be configured in ways thatare not listed. For the indication of elements, a singular or pluralform can be used, but it does not limit the scope of the disclosure andthe same teaching can apply to multiple objects, even if in the currentapplication an object is referred to in its singular form.

It will be appreciated that some embodiments describe the use of one ormore generic or specialized databases (such as “Exit Nodes Database”, orsimilar), that contains a collection of information that is organized sothat it can be easily accessed, managed and updated. Computer databasestypically contain aggregations of data records or files, in the currentcase, databases usually store different information and statistics aboutthe proxies or exit nodes, information about utilization threshold ofthe exit node provider. Such databases can also contain informationabout the users, requests performed, networks used, exit nodes used,types of exit nodes requested and similar data. Databases are structuredto facilitate the storage, retrieval, modification, and deletion of datain conjunction with various data-processing operations.

The Disclosure is provided to allow the reader to quickly ascertain thenature of the technical disclosure. It is submitted with theunderstanding that it will not be used to interpret or limit the scopeor meaning of the claims. In addition, in the foregoing DetailedDescription, it is demonstrated that multiple features are groupedtogether in various embodiments for the purpose of streamlining thedisclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment.

What is claimed is:
 1. A method of employing heuristic prediction of ahealth of an exit node pool for selecting and assigning a ranked exitnode to the exit node pool, for fulfillment of a user request, with theranked exit node being ranked based on an aggregated exit node history,the method comprising: setting a minimal threshold of the health of theexit node pool for executing the user request; heuristically predictinga future behavior of an exit node in the exit node pool, includingevaluating the health of the exit node pool, a quality of the exit node,and an impact of the exit node on an overall exit node pool health;heuristically determining a future health of the exit node pool based atleast in part on the future behavior of the exit node in the exit nodepool; comparing the future health to the minimal threshold; and, whereinif the future health is below the minimal threshold, the ranked exitnode is added to the exit node pool, or wherein if the future health ofthe exit node pool is above the minimal threshold, the exit node pool isnot modified.
 2. The method of claim 1, wherein, before the ranked exitnode is added to the exit node pool, a calculation is performed topredict whether adding the ranked exit node to the exit node pool willraise the health or lower the health of the exit node pool below theminimal threshold, and, wherein, the ranked exit node is only added ifthe health increases.
 3. The method of claim 1, wherein the exit node inthe exit node pool reports to a gateway by responding to test requestssent by the gateway.
 4. The method of claim 3, wherein the gatewaycollects test responses about the performance of the exit node or aplurality of exit nodes in a batch that contains all the test responsesreceived during a defined period of time.
 5. The method of claim 4,wherein the gateway reports the test responses to a logic unit thatproduces aggregated data about the exit node and stores the testresponses in the history database.
 6. The method of claim 5, wherein thelogic unit uses information from a history database to make heuristicpredictions about a future performance of the exit node and the exitnode pool.
 7. A method of employing heuristic prediction of a health ofan exit node pool for selecting and assigning a ranked exit node to theexit node pool, for fulfillment of a user request, with the ranked exitnode being ranked based on an aggregated exit node history, the methodcomprising: setting a minimal threshold of the health of the exit nodepool for executing the user request; heuristically predicting a futurebehavior of an exit node in the exit node pool, including evaluating thehealth of the exit node pool, a quality of the exit node, and an impactof the exit node on an overall exit node pool health; heuristicallydetermining a future health of the exit node pool based at least in parton the future behavior of the exit node in the exit node pool; comparingthe future health to the minimal threshold; and, wherein if the futurehealth is below the minimal threshold, the ranked exit node is added tothe exit node pool, or wherein if the future health of the exit nodepool is above the minimal threshold, the exit node pool is not modified;wherein the heuristically determining of the future health of the exitnode pool includes determining whether the future health will fall belowthe minimal threshold, and, if the future health will fall below theminimal threshold, requesting an other ranked exit node from an exitnode database.
 8. The method of claim 7, wherein the heuristicallypredicting comprises computational models such as neural networks,classification or regression trees, support vector machines, logisticregressors, Gaussian process models, or other computational models. 9.The method of claim 8, wherein the computational models can decidesuitable coefficients, loads, groupings, associations, boundaries,hyperparameters or other model traits that are utilized by generalpredictive ranking to make forecasts and by feeding aggregatedhistorical and service data as inputs into predictive ranking.
 10. Themethod of claim 7, wherein, when the exit node pool falls below theminimal threshold for quality, the logic unit determines the otherranked exit node best suitable to be included in the exit node poolbased on a predicted improvement score that the other ranked exit nodewould help the exit node pool achieve.
 11. The method of claim 10,wherein the logic unit determines whether the exit node pool qualitywould improve based on an improvement score, wherein the improvementscore indicates an assessed measurable difference in quality between thefuture health with the other ranked exit node in the pool and with thepool unmodified.
 12. The method of claim 11, wherein after thecalculation of the quality of the other ranked exit node, the otherranked exit node is: added to the exit node pool which would improvemost from addition of the other ranked exit node; reserved for later useand left idle or vacant if the other ranked exit node does not improveany exit node pool; or, reserved or assigned to the exit node poolnon-exclusively if the exit node pool is sufficiently populated.
 13. Themethod of claim 11, wherein based on a ranking of the exit nodes rankinga new exit node pool can be created if there are not enough ranked exitnodes that meet the parameters of the user request requirements orguarantee sufficient services.
 14. The method of claim 11, wherein thecalculating of the quality rate of the exit nodes in the exit node poolis triggered by any of the following or combinations thereof: expectedor predicted service, network, speed reliability drop, or increasednumber of user requests.
 15. A method of employing heuristic predictionof a health of an exit node pool for selecting and assigning a rankedexit node to the exit node pool, for fulfillment of a user request, withthe ranked exit node being ranked based on an aggregated exit nodehistory, the method comprising: setting a minimal threshold of thehealth of the exit node pool for executing the user request;heuristically predicting a future behavior of an exit node in the exitnode pool; heuristically determining a future health of the exit nodepool based at least in part on the future behavior of the exit node inthe exit node pool; comparing the future health to the minimalthreshold; and, wherein if the future health is below the minimalthreshold, the ranked exit node is added to the exit node pool, orwherein if the future health of the exit node pool is above the minimalthreshold, the exit node pool is not modified; wherein the heuristicallypredicting of the future health of the exit node pool and the exit nodequality can be based at least on one of: frequency, intervals, andschedule at which static parameters are changed (such as geolocation ofexit node, exit node IP type, consent to participate in Content DeliveryNetwork proxying, or any combination thereof), dynamic parameters (suchas time seen, session duration, activity timestamps, total traffic,traffic over time, response time, latency, connection to target, batterylife, geographical location, connection, IP type, consent to participatein a distributed CDN access model, or any combination thereof);aggregated dynamic parameters over any period of time (average speed,average session duration, timestamps, average traffic, average responsetime, average latency, most/least visited targets, error rate with aparticular target, variations in which median and percentile groups areused, or any combination thereof); or, exit node service history (suchas total pools per period, total users, total idle connection time,total pool time, average pool time, average pool change rate, averagepool health, or any combination thereof).
 16. The method of claim 1,wherein historical data is used to predict if the exit node hassufficient speed, reliability to perform streaming activities throughthe exit node.
 17. The method of claim 1 wherein the exit node can bereplaced within the exit node pool if any condition triggeringreplacement occurs, including but not limited to: the exit nodedisconnecting, the health of the exit node pool dropping or beingpredicted to drop, a change in overall quality of the exit node, arequest parameter change requested by a user, an overload ofinfrastructure, an increase in either number or volume of the userrequest or of the exit node, any combination of these events.
 18. Themethod of claim 1, wherein the minimal threshold can be establishedindividually by a service provider decision based on requirements of theuser request, such as service speed, reliability, response time, trafficload, schedule, compatibility with third party services.
 19. The methodof claim 1, wherein a calculation of the health of the exit node pool isperformed by employing machine learning algorithms.
 20. The method ofclaim 19, wherein if the health of the exit node pool is below theminimal threshold, the health of the exit node pool can be increased byone or more of the following: adding the ranked exit node in advance,predicting that the health of the exit node pool will fall below theminimal threshold within a pre-defined future period of time; forming anew exit node pool, wherein the new exit node pool can have overlappingexit nodes with the exit node pool; replacing the exit node pool with adifferent exit node pool completely; replacing all the exit nodes in theexit node pool with ranked exit nodes.
 21. The method of claim 1,wherein if the ranked edit node is not available, a new exit nodewithout an aggregated history is added to the exit node pool instead ofthe ranked exit node.